{"id":"W4211107845","doi":"10.2200/s00999ed3v01y202003hlt046","title":"Natural Language Processing for Social Media, Third Edition","year":2020,"lang":"en","type":"article","venue":"Synthesis lectures on human language technologies","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Social media; Interpersonal communication; Natural (archaeology); Computer science; Psychology; Sociology; World Wide Web; Communication; History","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000154207,0.0002178468,0.0002937013,0.0002247703,0.0004257099,0.0002574567,0.001008426,0.0001596354,0.00003140987],"category_scores_gemma":[0.0009673967,0.0001742506,0.0001761216,0.0004000444,0.00009649553,0.0002215064,0.0001718071,0.0002507184,0.00001684656],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003662306,"about_ca_system_score_gemma":0.0000182579,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003393772,"about_ca_topic_score_gemma":0.00001534268,"domain_scores_codex":[0.9986086,0.00003843287,0.000222919,0.000492611,0.0003137677,0.0003236361],"domain_scores_gemma":[0.9991626,0.0003215301,0.0001642918,0.0002780206,0.00004459923,0.00002896064],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003130539,0.00006718574,0.00002963645,0.0001152238,0.0001003203,0.00003718334,0.03163395,0.0000201787,0.07347572,0.01203945,0.01561424,0.8668356],"study_design_scores_gemma":[0.0008769468,0.0002704653,0.0007746438,0.0002029452,0.0001404946,0.000006694722,0.03906368,0.0220855,0.9278591,0.004546876,0.002819892,0.00135281],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.636974,0.02579425,0.230589,0.07264744,0.001470863,0.002022547,0.0001117522,0.02559616,0.004793962],"genre_scores_gemma":[0.9920188,0.000004691767,0.00684615,0.0005617151,0.0003987906,0.0001102909,0.00001912635,0.00001777453,0.00002261731],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8654828,"threshold_uncertainty_score":0.7105735,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03886129174228645,"score_gpt":0.3072510998608941,"score_spread":0.2683898081186076,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}